Winter Term 2017-18 / Neural Inf Process

Mathematical Basics for Computational Neuroscientists

Lecturer
Bethge et al.

Credits
3.0

Course content / topics

ObjectivesThe main goal of this pre-course is to set the stage for the theory lectures of the Graduate School of Neural Information Processing such as Machine Learning, Neural Coding, and Neural Data Analysis. A strong focus is placed on applied linear algebra and probabilistic modeling. Specific topics include multivariate random variables and matrix factorizations, statistics and optimization, dynamical systems and Fourier transform.